Supervised ReID. The transformer-based baseline proposed in [1]} is used as our baseline in this paper. That is to say, none of the overlapping patch embedding, jigsaw patch module or side information embedding is included here. It is noticed that DINO models should be fine-tuned with a small learning rate and a longer warm-up process, so we recommend setting the learning rate to \(lr = 0.0004 \times \frac{batchsize}{64}\)  and warm-up epochs to be 20. All other settings are same with the original paper.
